Randy D. Dinkins

3.0k total citations
60 papers, 1.9k citations indexed

About

Randy D. Dinkins is a scholar working on Plant Science, Molecular Biology and Ecology, Evolution, Behavior and Systematics. According to data from OpenAlex, Randy D. Dinkins has authored 60 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 39 papers in Plant Science, 32 papers in Molecular Biology and 13 papers in Ecology, Evolution, Behavior and Systematics. Recurrent topics in Randy D. Dinkins's work include Plant Molecular Biology Research (17 papers), Plant and fungal interactions (13 papers) and Plant tissue culture and regeneration (10 papers). Randy D. Dinkins is often cited by papers focused on Plant Molecular Biology Research (17 papers), Plant and fungal interactions (13 papers) and Plant tissue culture and regeneration (10 papers). Randy D. Dinkins collaborates with scholars based in United States, China and Hungary. Randy D. Dinkins's co-authors include G. B. Collins, Christopher L. Schardl, Padmaja Nagabhyru, M. S. Srinivasa Reddy, Arthur G. Hunt, Charles W. Bacon, Constance L. Wood, Dhiraj Thakare, S. Kumudini and Guiliang Tang and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Biological Chemistry and The Plant Cell.

In The Last Decade

Randy D. Dinkins

57 papers receiving 1.8k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Randy D. Dinkins United States 28 1.4k 1.1k 286 188 125 60 1.9k
Theodor Lange Germany 29 2.2k 1.6× 1.7k 1.6× 167 0.6× 70 0.4× 60 0.5× 48 2.6k
Daguang Cai Germany 27 1.9k 1.4× 950 0.9× 93 0.3× 84 0.4× 170 1.4× 59 2.2k
Christoph Ringli Switzerland 31 2.7k 2.0× 2.3k 2.1× 217 0.8× 75 0.4× 104 0.8× 45 3.1k
Kyung-Hwan Han United States 21 1.2k 0.9× 1.2k 1.1× 66 0.2× 118 0.6× 66 0.5× 33 1.6k
Jong Seob Lee South Korea 25 3.5k 2.6× 2.5k 2.3× 261 0.9× 179 1.0× 56 0.4× 51 4.0k
Ruth C. Martin United States 24 1.5k 1.1× 1.2k 1.1× 136 0.5× 74 0.4× 69 0.6× 70 2.0k
Clelia De‐la‐Peña Mexico 21 1.4k 1.1× 1.1k 1.0× 108 0.4× 91 0.5× 58 0.5× 61 1.8k
Momoko Ikeuchi Japan 22 2.3k 1.7× 2.3k 2.1× 141 0.5× 83 0.4× 99 0.8× 36 2.9k
Max O. Ruegger United States 14 1.6k 1.2× 1.9k 1.8× 67 0.2× 328 1.7× 57 0.5× 15 2.4k
Xiangzong Meng China 21 4.0k 2.9× 2.2k 2.0× 104 0.4× 88 0.5× 306 2.4× 41 4.4k

Countries citing papers authored by Randy D. Dinkins

Since Specialization
Citations

This map shows the geographic impact of Randy D. Dinkins's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Randy D. Dinkins with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Randy D. Dinkins more than expected).

Fields of papers citing papers by Randy D. Dinkins

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Randy D. Dinkins. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Randy D. Dinkins. The network helps show where Randy D. Dinkins may publish in the future.

Co-authorship network of co-authors of Randy D. Dinkins

This figure shows the co-authorship network connecting the top 25 collaborators of Randy D. Dinkins. A scholar is included among the top collaborators of Randy D. Dinkins based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Randy D. Dinkins. Randy D. Dinkins is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Qin, Qiulin, Jingyin Yu, Shengming Yang, et al.. (2024). Species-specific microsymbiont discrimination mediated by a Medicago receptor kinase. Science Advances. 10(31). eadp6436–eadp6436. 3 indexed citations
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Liu, Jinge, Qiulin Qin, Shengming Yang, et al.. (2022). Paired Medicago receptors mediate broad-spectrum resistance to nodulation by Sinorhizobium meliloti carrying a species-specific gene. Proceedings of the National Academy of Sciences. 119(51). e2214703119–e2214703119. 12 indexed citations
5.
Nagabhyru, Padmaja, Randy D. Dinkins, & Christopher L. Schardl. (2022). Transcriptome analysis of Epichloë strains in tall fescue in response to drought stress. Mycologia. 114(4). 697–712. 10 indexed citations
6.
Dinkins, Randy D., et al.. (2022). Expression and Variation of the Genes Involved in Rhizobium Nodulation in Red Clover. Plants. 11(21). 2888–2888. 3 indexed citations
7.
Chakrabarti, Manohar, Padmaja Nagabhyru, Christopher L. Schardl, & Randy D. Dinkins. (2022). Differential gene expression in tall fescue tissues in response to water deficit. The Plant Genome. 15(2). e20199–e20199. 16 indexed citations
8.
Dinkins, Randy D., et al.. (2022). Field performance of a red clover germplasm selected for increased tolerance to 2,4-D. Weed Technology. 36(6). 831–837.
9.
Dinkins, Randy D., Jack P. Goodman, Jinge Liu, et al.. (2021). Isoflavone levels, nodulation and gene expression profiles of a CRISPR/Cas9 deletion mutant in the isoflavone synthase gene of red clover. Plant Cell Reports. 40(3). 517–528. 27 indexed citations
10.
Slaughter, Lindsey C., et al.. (2019). Tall Fescue and E. coenophiala Genetics Influence Root-Associated Soil Fungi in a Temperate Grassland. Frontiers in Microbiology. 10. 2380–2380. 10 indexed citations
11.
Nagabhyru, Padmaja, Randy D. Dinkins, & Christopher L. Schardl. (2018). Transcriptomics of Epichloë-Grass Symbioses in Host Vegetative and Reproductive Stages. Molecular Plant-Microbe Interactions. 32(2). 194–207. 22 indexed citations
12.
Nayak, Nihar R., Andrea Putnam, Balasubrahmanyam Addepalli, et al.. (2013). An Arabidopsis ATP-Dependent, DEAD-Box RNA Helicase Loses Activity upon IsoAsp Formation but Is Restored by PROTEIN ISOASPARTYL METHYLTRANSFERASE. The Plant Cell. 25(7). 2573–2586. 29 indexed citations
13.
Thakare, Dhiraj, S. Kumudini, & Randy D. Dinkins. (2011). The alleles at the E1 locus impact the expression pattern of two soybean FT-like genes shown to induce flowering in Arabidopsis. Planta. 234(5). 933–943. 35 indexed citations
14.
Chen, Tingsu, Nihar R. Nayak, Jonathan D. Lowenson, et al.. (2010). Substrates of the Arabidopsis thaliana Protein Isoaspartyl Methyltransferase 1 Identified Using Phage Display and Biopanning. Journal of Biological Chemistry. 285(48). 37281–37292. 37 indexed citations
15.
Thakare, Dhiraj, S. Kumudini, & Randy D. Dinkins. (2010). Expression of flowering-time genes in soybean E1 near-isogenic lines under short and long day conditions. Planta. 231(4). 951–963. 32 indexed citations
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Collins, G. B., et al.. (2005). Targeted overexpression of the Escherichia coli MinC protein in higher plants results in abnormal chloroplasts. Plant Cell Reports. 25(4). 341–348. 12 indexed citations
18.
Dinkins, Randy D., et al.. (2003). Expression and deletion analysis of an Arabidopsis SUPERMAN-like zinc finger gene. Plant Science. 165(1). 33–41. 26 indexed citations
19.
Dinkins, Randy D., et al.. (1998). Factors affecting soybean cotyledonary node transformation. Plant Cell Reports. 18(3-4). 180–186. 99 indexed citations
20.
Dinkins, Randy D., et al.. (1994). A nuclear photosynthetic electron transport mutant of Arabidopsis thaliana with altered expression of the chloroplast petA gene. Current Genetics. 25(3). 282–288. 18 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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